{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81931b00",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f98abedb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from __future__ import annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1322ccdf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# debug\n",
    "import sys\n",
    "\n",
    "print(sys.executable)\n",
    "print(sys.version_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4302062f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# main imports\n",
    "import holoviews as hv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "hv.extension(\"bokeh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0e2d11",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Open a dataset\n",
    "\n",
    "Thalassa supports the output of several solvers (e.g. Schism, ADCIRC). \n",
    "In order to do so, it converts the output of these files to a common \"schema\".\n",
    "This process is called \"normalization\".\n",
    "\n",
    "> \"normalization\" == Renaming of dimensions and variables\n",
    "\n",
    "The most convenient way to apply this normalization process is to use the `api.open_dataset()` function which is a wrapper around `xr.open_dataset()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff903d5f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from thalassa import api\n",
    "\n",
    "api.open_dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5178d63",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename = \"../tests/data/fort.63.nc\"\n",
    "ds = api.open_dataset(filename)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6c402a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Main API\n",
    "\n",
    "`thalassa` supports several types of graphs, including:\n",
    "    \n",
    "- A visualization of the variables at various timestamps and/or layers.\n",
    "- A visualization of the mesh\n",
    "- Extraction of timeseries from specific points\n",
    "\n",
    "The following cell shows the basic usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d18c80",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#variable, layer, timestamp = \"salt\", 40, \n",
    "#variable, layer, timestamp = \"depth\", None, None\n",
    "variable, layer, timestamp = \"zeta\", None, ds.time.values[4]\n",
    "\n",
    "# The trimesh is the most basic object. This is what you need to create all the others graphs\n",
    "# It is on this object that you specify the timestamp and/or the layer.\n",
    "trimesh = api.create_trimesh(ds.sel(time=timestamp), variable=variable)\n",
    "\n",
    "# The wireframe is the representation of the mesh\n",
    "wireframe = api.get_wireframe(trimesh)\n",
    "\n",
    "# The tiles is using the tiling service from Open Street maps\n",
    "tiles =  api.get_tiles() \n",
    "\n",
    "# The raster object is the basic Map that visualizes the variable. \n",
    "# You can specify things like the colorbar limits and/or the extents\n",
    "#raster = api.get_raster(trimesh, clim_min=0, clim_max=15)\n",
    "raster = api.get_raster(trimesh)\n",
    "\n",
    "# The pointer/tap timeseries extract the timeseries of a specific node from the xr.Dataset and visualize it.\n",
    "pointer_dmap = api.get_pointer_timeseries(ds=ds, variable=variable, source_raster=raster)\n",
    "tap_dmap = api.get_tap_timeseries(ds=ds, variable=variable, source_raster=raster)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d408e8dc",
   "metadata": {},
   "source": [
    "### Let's visualize!\n",
    "\n",
    "After you render the layout, move the mouse over the map and click on it. This will fill in the `pointer_dmap` and the `tap_dmap`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61d335a4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "raster_layout = tiles * raster.opts(width=600, cmap=\"viridis\") \n",
    "raster_layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd80deda",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    \n",
    "    tap_dmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d0e9454",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mesh_layout = tiles * wireframe.opts(width=600)\n",
    "mesh_layout"
   ]
  }
 ],
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